Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/10291
Title: A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction
Authors: Sales, Márcio H. 
de Bruin, Sytze 
Herold, M. 
Kyriakidis, Phaedon 
Souza, Carlos Moreira 
Keywords: Land cover models;Deforestation;Spatiotemporal modeling;Hurdle models
Category: Civil Engineering;Civil Engineering
Field: Engineering and Technology
Issue Date: Aug-2017
Publisher: Science Direct
Source: Spatial Statistics, Volume 21, Part A, 2017, Pages 304-318
metadata.dc.doi: https://doi.org/10.1016/j.spasta.2017.06.003
Abstract: This paper introduces and tests a geostatistical spatiotemporal hurdle approach for predicting the spatial distribution of future deforestation (one to three years ahead in time). The method accounts for neighborhood effects by modeling the auto-correlation of occurrence and intensity of deforestation, using a spatiotemporal geostatistical specification. Deforestation observations are modeled as a function of pertinent control variables, such as distance to roads and protected areas, and the model accounts for space–time autocorrelated residuals with non-stationary variance. Applied to the Brazilian Amazon, the model predicted the locations of new deforestation events with over 90% agreement. In addition, 100% of the deforestation intensity values were contained in the model’s confidence bounds. The features of the model and validation results qualify the model as a strong candidate for short-term deforestation modeling.
URI: http://ktisis.cut.ac.cy/handle/10488/10291
ISSN: 22116753
Rights: © 2017 Elsevier B.V. All rights reserved.
Type: Article
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